The project is about the Modern Portfolio Theory (MPT) based analysis and optimization of a given portfolio’s returns based on individual investors’ risk tolerance. It assumes it has a use case in a real world utility for business roles like a Portfolio Manager or Quantitative Analyst in any Fintech firm.
The project tries to solve a typical business problem of a Portfolio Manager to find an optimal construction of an investment portfolio.
Let's take a business scenario as described below.
User Story:
As a Portfolio Manager of a Fintech firm, he/she should be able to:
- Construct a portfolio of assets based on market index to maximize profit returns.
- Identify the optimal weights or mix (percentage of investment) of constituent assets such that risk can be minimized.
- Analyze the market return metrics through statistical visualizations and evidence.
To measure the functioning of the application, let's assume further to check the following conditions.
Acceptance Criteria:
- Allow the user to run the application from a Jupyter Notebook.
- Show maximum possible returns, given a certain risk tolerance as defined by the individual investor.
- Show minimum possible risk, given a certain risk tolerance for a target return as desired by the individual investor.
The Modern Portfolio Theory has two main fundamentals:
- a portfolio’s total risk and return profile is more important than the risk/return profile of any individual investment, and
- it is possible for an investor to build a diversified portfolio comprising of multiple assets or investments that will maximize returns while limiting risk.
The above points are built upon an understanding of three key concepts:
- the risk-return relationship,
- the role of diversification in building an investment portfolio, and
- the efficient frontier representing the combination offering the best possible expected return for given risk level.
This project leverages python 3.7.* with the following additional packages:
- Jupyter Notebook - The main module of the Financial Planner is written in Jupyter Notebook.
- Conda - Conda environment is recommended to have Pandas library and other dependencies pre-installed.
Required Libraries:
You may need the following libraries to work with the application.
- Pandas - pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.
- NumPy - NumPy is the fundamental package for scientific computing in Python.
- yfinance - Download market data from Yahoo! Finance's API
- SQLAlchemy - SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.
To use the application, clone the repository and run the mpt.ipynb with Jupyter notebook.
From the Git Bash terminal, make sure to 'activate conda' and appropriate virtual enivorment. Next, launch the JupyterLab web-based interactive development environment (IDE) interface by typing at the prompt:
> jupyter labThen, browse to mpt.ipynb starter code file to run the program. See the image below for a quick hint.
Note:
- The results of data and portfolio analysis are stored in images/ folder for reference when the project was unit tested.
- Each time you run the mpt.ipynb, the raw data from Yahoo! Finance API will be stored in data/raw_frame_close.csv file.
Peers of FinTech Blended Boot Camp, Columbia Engineering (2022-23 Batch)
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